POS0890 USING POLYGENIC RISK SCORES TO AID DIAGNOSIS OF PATIENTS WITH EARLY INFLAMMATORY ARTHRITIS: RESULTS FROM THE NORFOLK ARTHRITIS REGISTER
Authors: R. M. Hum, S. Sharma, M. Stadler, S. Viatte, P. Ho, N. Nair, C. Shi, C. F. Yap, M. Soomro, D. Plant, J. Humphreys, A. Macgregor, M. Yates, S. Verstappen, J. Bowes, A. Barton
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> There is growing evidence that genetic information can offer valuable information to a clinician in a diagnostic setting, and that it is feasible that genetic data would be of benefit to the rheumatology outpatient setting by aiding early diagnosis. A genetic probability tool (G-PROB) has recently been developed to aid diagnosis using existing knowledge of disease-associated genetic variants but has only been tested in a limited capacity. <h3>Objectives</h3> Our aim was to assess whether G-PROB could aid diagnosis in the rheumatology outpatient setting using data from the Norfolk Arthritis Register (NOAR), a large prospective observational cohort of patients presenting with early inflammatory arthritis where diagnosis at baseline is unclear. <h3>Methods</h3> Genotypes and follow-up clinician diagnoses were obtained from patients from NOAR. Six G-probabilities (0-100%) were created for each patient based on known disease-associated odds ratios (ORs) of published genetic risk variants, each corresponding to one disease of rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), spondyloarthropathy (SpA), gout or "other rheumatological diseases". Performance of the G-probabilities compared with the final (follow-up) clinician diagnosis was assessed. <h3>Results</h3> G-PROB was tested on 972 patients from NOAR. Calibration of G-probabilities with clinician diagnosis was high, with regression coefficients of 1·03, where 1·00 represents perfect calibration. G-probabilities discriminated clinician diagnosis with pooled areas under the curve (95% CI) of 0·84 (0·81 to 0·86). G-probabilities &lt;5% corresponded to a negative predictive value (NPV) of 96% where it was possible to suggest at least one unlikely diagnosis for 99·9% of patients, two or more diseases for 94% of patients, and three or more diseases for 54% of patients. G-probabilities &gt;50% corresponded to a positive predictive value (PPV) of 74%. In 57% of patients, the disease with the highest G-probability corresponded to clinician diagnosis. <h3>Conclusion</h3> G-PROB successfully converts complex genetic information into meaningful, and interpretable conditional probabilities which may be especially helpful at suggesting unlikely diagnoses in the rheumatology outpatient setting. <h3>Reference</h3> [1]Knevel, Rachel et al. "Using genetics to prioritize diagnoses for rheumatology outpatients with inflammatory arthritis." Science translational medicine vol. 12,545 (2020): eaay1548. doi:10.1126/scitranslmed.aay1548 <h3>Acknowledgements</h3> RMH and SS are joint first authors. JB and AB are joint last authors. The authors would like to thank the patients involved in NOAR and all supporting staff involved in its implementation. The authors also thank Prof Deborah Symmons for her involvement in the inception of the NOAR cohort. RMH is supported by an Academic Clinical Fellowship awarded by the National Institute for Health Research as part of the Integrated Academic Training (IAT) programme. <h3>Disclosure of Interests</h3> None Declared.